The core of the open-world detection problem, as pointed out by Kim [1]}, lies in the detector training pipeline: regarding hidden objects as background. This training scheme is common in both two-stage and one-stage detectors. However, none of the approaches listed above solves this issue. Our approach takes the first step in addressing background suppression via novel data augmentation strategies and shows remarkable improvements over baselines despite its simplicity.
